Overview

Brought to you by YData

Dataset statistics

Number of variables54
Number of observations125
Missing cells44
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.9 KiB
Average record size in memory433.1 B

Variable types

Numeric11
Categorical36
Text7

Alerts

Gender is highly overall correlated with weightHigh correlation
eating_changes_coded is highly overall correlated with eating_changes_coded1High correlation
eating_changes_coded1 is highly overall correlated with eating_changes_codedHigh correlation
healthy_feeling is highly overall correlated with life_rewardingHigh correlation
indian_food is highly overall correlated with persian_food and 1 other fieldsHigh correlation
life_rewarding is highly overall correlated with healthy_feelingHigh correlation
persian_food is highly overall correlated with indian_foodHigh correlation
thai_food is highly overall correlated with indian_foodHigh correlation
weight is highly overall correlated with GenderHigh correlation
fries is highly imbalanced (57.0%) Imbalance
comfort_food_reasons has 2 (1.6%) missing values Missing
cook has 3 (2.4%) missing values Missing
drink has 2 (1.6%) missing values Missing
eating_changes has 3 (2.4%) missing values Missing
employment has 9 (7.2%) missing values Missing
father_profession has 3 (2.4%) missing values Missing
fav_cuisine has 2 (1.6%) missing values Missing
fav_food has 2 (1.6%) missing values Missing
mother_education has 3 (2.4%) missing values Missing
mother_profession has 2 (1.6%) missing values Missing
sports has 2 (1.6%) missing values Missing
fav_cuisine_coded has 6 (4.8%) zeros Zeros

Reproduction

Analysis started2025-04-11 02:54:08.674711
Analysis finished2025-04-11 02:54:21.612240
Duration12.94 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

GPA
Real number (ℝ)

Distinct37
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4186529
Minimum2.2
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:21.683244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile2.8
Q13.2
median3.5
Q33.7
95-th percentile3.9
Maximum4
Range1.8
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.38365649
Coefficient of variation (CV)0.11222446
Kurtosis0.43658389
Mean3.4186529
Median Absolute Deviation (MAD)0.27
Skewness-0.77246485
Sum427.33161
Variance0.1471923
MonotonicityNot monotonic
2025-04-10T23:54:21.794239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
3.5 13
 
10.4%
3 11
 
8.8%
3.2 10
 
8.0%
3.7 10
 
8.0%
3.3 9
 
7.2%
3.4 9
 
7.2%
3.9 7
 
5.6%
3.6 7
 
5.6%
3.8 6
 
4.8%
2.8 5
 
4.0%
Other values (27) 38
30.4%
ValueCountFrequency (%)
2.2 1
 
0.8%
2.25 1
 
0.8%
2.4 1
 
0.8%
2.6 2
 
1.6%
2.71 1
 
0.8%
2.8 5
4.0%
2.9 2
 
1.6%
3 11
8.8%
3.1 3
 
2.4%
3.2 10
8.0%
ValueCountFrequency (%)
4 4
3.2%
3.92 1
 
0.8%
3.904 1
 
0.8%
3.9 7
5.6%
3.89 1
 
0.8%
3.882 1
 
0.8%
3.87 1
 
0.8%
3.83 2
 
1.6%
3.8 6
4.8%
3.79 1
 
0.8%

Gender
Categorical

High correlation 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
76 
2
49 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Length

2025-04-10T23:54:21.892239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:21.971240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring characters

ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 76
60.8%
2 49
39.2%

breakfast
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
111 
2
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Length

2025-04-10T23:54:22.047243image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:22.118239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring characters

ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 111
88.8%
2 14
 
11.2%

calories_day
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3.0
63 
4.0
23 
2.0
20 
0.0
19 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row3.0
3rd row4.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
3.0 63
50.4%
4.0 23
 
18.4%
2.0 20
 
16.0%
0.0 19
 
15.2%

Length

2025-04-10T23:54:22.196239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:22.275241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 63
50.4%
4.0 23
 
18.4%
2.0 20
 
16.0%
0.0 19
 
15.2%

Most occurring characters

ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 144
38.4%
. 125
33.3%
3 63
16.8%
4 23
 
6.1%
2 20
 
5.3%

calories_scone
Categorical

Distinct3
Distinct (%)2.4%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
420.0
79 
980.0
23 
315.0
22 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters620
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row315.0
2nd row420.0
3rd row420.0
4th row420.0
5th row420.0

Common Values

ValueCountFrequency (%)
420.0 79
63.2%
980.0 23
 
18.4%
315.0 22
 
17.6%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:22.364239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:22.438241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
420.0 79
63.7%
980.0 23
 
18.5%
315.0 22
 
17.7%

Most occurring characters

ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 226
36.5%
. 124
20.0%
4 79
 
12.7%
2 79
 
12.7%
9 23
 
3.7%
8 23
 
3.7%
3 22
 
3.5%
1 22
 
3.5%
5 22
 
3.5%

coffee
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
94 
1
31 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Length

2025-04-10T23:54:22.522239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:22.597239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring characters

ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 94
75.2%
1 31
 
24.8%
Distinct124
Distinct (%)100.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
2025-04-10T23:54:22.717239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length97
Median length48
Mean length34.766129
Min length4

Characters and Unicode

Total characters4311
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique124 ?
Unique (%)100.0%

Sample

1st rownone
2nd rowchocolate, chips, ice cream
3rd rowfrozen yogurt, pizza, fast food
4th rowPizza, Mac and cheese, ice cream
5th rowIce cream, chocolate, chips
ValueCountFrequency (%)
ice 50
 
7.9%
cream 47
 
7.4%
and 40
 
6.3%
pizza 40
 
6.3%
chocolate 35
 
5.5%
chips 34
 
5.3%
cheese 22
 
3.5%
cookies 19
 
3.0%
mac 17
 
2.7%
chicken 14
 
2.2%
Other values (169) 318
50.0%
2025-04-10T23:54:22.959882image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4311
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
552
12.8%
e 441
 
10.2%
a 354
 
8.2%
c 344
 
8.0%
, 260
 
6.0%
s 247
 
5.7%
o 245
 
5.7%
i 240
 
5.6%
r 177
 
4.1%
n 166
 
3.9%
Other values (42) 1285
29.8%

comfort_food_reasons
Text

Missing 

Distinct106
Distinct (%)86.2%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T23:54:23.110392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length137
Median length59
Mean length26.902439
Min length4

Characters and Unicode

Total characters3309
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique97 ?
Unique (%)78.9%

Sample

1st rowwe dont have comfort
2nd rowStress, bored, anger
3rd rowstress, sadness
4th rowBoredom
5th rowStress, boredom, cravings
ValueCountFrequency (%)
boredom 74
 
14.6%
sadness 42
 
8.3%
stress 33
 
6.5%
and 26
 
5.1%
i 19
 
3.7%
when 12
 
2.4%
anger 10
 
2.0%
comfort 10
 
2.0%
happiness 9
 
1.8%
bored 9
 
1.8%
Other values (158) 264
52.0%
2025-04-10T23:54:23.394392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3309
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
424
12.8%
s 344
 
10.4%
e 339
 
10.2%
o 287
 
8.7%
r 210
 
6.3%
d 203
 
6.1%
a 189
 
5.7%
n 189
 
5.7%
t 131
 
4.0%
m 128
 
3.9%
Other values (45) 865
26.1%

cook
Categorical

Missing 

Distinct5
Distinct (%)4.1%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
3.0
49 
2.0
34 
4.0
18 
1.0
13 
5.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row1.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
3.0 49
39.2%
2.0 34
27.2%
4.0 18
 
14.4%
1.0 13
 
10.4%
5.0 8
 
6.4%
(Missing) 3
 
2.4%

Length

2025-04-10T23:54:23.497392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:23.577392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3.0 49
40.2%
2.0 34
27.9%
4.0 18
 
14.8%
1.0 13
 
10.7%
5.0 8
 
6.6%

Most occurring characters

ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
3 49
13.4%
2 34
 
9.3%
4 18
 
4.9%
1 13
 
3.6%
5 8
 
2.2%
Distinct8
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.688
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:23.655392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile7
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.9109869
Coefficient of variation (CV)0.71093263
Kurtosis3.4213848
Mean2.688
Median Absolute Deviation (MAD)1
Skewness1.922052
Sum336
Variance3.651871
MonotonicityNot monotonic
2025-04-10T23:54:23.736397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 53
42.4%
1 28
22.4%
3 23
18.4%
5 7
 
5.6%
9 5
 
4.0%
7 5
 
4.0%
4 3
 
2.4%
6 1
 
0.8%
ValueCountFrequency (%)
1 28
22.4%
2 53
42.4%
3 23
18.4%
4 3
 
2.4%
5 7
 
5.6%
6 1
 
0.8%
7 5
 
4.0%
9 5
 
4.0%
ValueCountFrequency (%)
9 5
 
4.0%
7 5
 
4.0%
6 1
 
0.8%
5 7
 
5.6%
4 3
 
2.4%
3 23
18.4%
2 53
42.4%
1 28
22.4%

cuisine
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.016
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:23.814393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.827143
Coefficient of variation (CV)0.90632094
Kurtosis0.72852936
Mean2.016
Median Absolute Deviation (MAD)0
Skewness1.5641079
Sum252
Variance3.3384516
MonotonicityNot monotonic
2025-04-10T23:54:24.139392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 86
68.8%
6 19
 
15.2%
2 13
 
10.4%
3 3
 
2.4%
4 3
 
2.4%
5 1
 
0.8%
ValueCountFrequency (%)
1 86
68.8%
2 13
 
10.4%
3 3
 
2.4%
4 3
 
2.4%
5 1
 
0.8%
6 19
 
15.2%
ValueCountFrequency (%)
6 19
 
15.2%
5 1
 
0.8%
4 3
 
2.4%
3 3
 
2.4%
2 13
 
10.4%
1 86
68.8%
Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
60 
1
50 
3
10 
4
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Length

2025-04-10T23:54:24.220392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:24.294393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 60
48.0%
1 50
40.0%
3 10
 
8.0%
4 5
 
4.0%

drink
Categorical

Missing 

Distinct2
Distinct (%)1.6%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2.0
69 
1.0
54 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row2.0
3rd row1.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 69
55.2%
1.0 54
43.2%
(Missing) 2
 
1.6%

Length

2025-04-10T23:54:24.379393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:24.449393image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 69
56.1%
1.0 54
43.9%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
2 69
18.7%
1 54
14.6%

eating_changes
Text

Missing 

Distinct121
Distinct (%)99.2%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
2025-04-10T23:54:24.594392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length318
Median length85
Mean length58.434426
Min length4

Characters and Unicode

Total characters7129
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)98.4%

Sample

1st roweat faster
2nd rowI eat out more than usual.
3rd rowsometimes choosing to eat fast food instead of cooking simply for convenience
4th rowAccepting cheap and premade/store bought foods
5th rowI have eaten generally the same foods but I do find myself eating the same food frequently due to what I have found I like from egan and the laker.
ValueCountFrequency (%)
i 122
 
8.7%
eat 58
 
4.1%
more 58
 
4.1%
to 46
 
3.3%
and 43
 
3.1%
have 33
 
2.4%
a 30
 
2.1%
food 26
 
1.9%
less 25
 
1.8%
the 24
 
1.7%
Other values (362) 939
66.9%
2025-04-10T23:54:24.885394image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1331
18.7%
e 771
 
10.8%
a 571
 
8.0%
o 490
 
6.9%
t 488
 
6.8%
n 354
 
5.0%
s 332
 
4.7%
i 282
 
4.0%
l 257
 
3.6%
r 249
 
3.5%
Other values (42) 2004
28.1%

eating_changes_coded
Categorical

High correlation 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
75 
2
36 
3
11 
4
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row3

Common Values

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Length

2025-04-10T23:54:24.986392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:25.061392image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 11
 
8.8%
4 3
 
2.4%

eating_changes_coded1
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)10.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.552
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:25.135944image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile11
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.5477884
Coefficient of variation (CV)0.55970747
Kurtosis1.9073443
Mean4.552
Median Absolute Deviation (MAD)1
Skewness1.554581
Sum569
Variance6.4912258
MonotonicityNot monotonic
2025-04-10T23:54:25.213973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 44
35.2%
5 32
25.6%
2 15
 
12.0%
4 12
 
9.6%
8 5
 
4.0%
11 5
 
4.0%
7 3
 
2.4%
10 3
 
2.4%
12 2
 
1.6%
1 1
 
0.8%
Other values (3) 3
 
2.4%
ValueCountFrequency (%)
1 1
 
0.8%
2 15
 
12.0%
3 44
35.2%
4 12
 
9.6%
5 32
25.6%
6 1
 
0.8%
7 3
 
2.4%
8 5
 
4.0%
9 1
 
0.8%
10 3
 
2.4%
ValueCountFrequency (%)
13 1
 
0.8%
12 2
 
1.6%
11 5
 
4.0%
10 3
 
2.4%
9 1
 
0.8%
8 5
 
4.0%
7 3
 
2.4%
6 1
 
0.8%
5 32
25.6%
4 12
 
9.6%

eating_out
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
60 
3
24 
1
16 
4
13 
5
12 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Length

2025-04-10T23:54:25.297978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:25.376973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring characters

ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 60
48.0%
3 24
 
19.2%
1 16
 
12.8%
4 13
 
10.4%
5 12
 
9.6%

employment
Categorical

Missing 

Distinct3
Distinct (%)2.6%
Missing9
Missing (%)7.2%
Memory size1.1 KiB
2.0
60 
3.0
54 
1.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters348
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row2.0
3rd row3.0
4th row3.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 60
48.0%
3.0 54
43.2%
1.0 2
 
1.6%
(Missing) 9
 
7.2%

Length

2025-04-10T23:54:25.464975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:25.536973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0 60
51.7%
3.0 54
46.6%
1.0 2
 
1.7%

Most occurring characters

ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 348
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 116
33.3%
0 116
33.3%
2 60
17.2%
3 54
15.5%
1 2
 
0.6%

ethnic_food
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
42 
4
36 
3
25 
2
17 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row4
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Length

2025-04-10T23:54:25.614974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:25.694975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring characters

ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 42
33.6%
4 36
28.8%
3 25
20.0%
2 17
13.6%
1 5
 
4.0%

exercise
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1.0
57 
2.0
44 
0.0
13 
3.0
11 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 57
45.6%
2.0 44
35.2%
0.0 13
 
10.4%
3.0 11
 
8.8%

Length

2025-04-10T23:54:25.783974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:25.861974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 57
45.6%
2.0 44
35.2%
0.0 13
 
10.4%
3.0 11
 
8.8%

Most occurring characters

ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 138
36.8%
. 125
33.3%
1 57
15.2%
2 44
 
11.7%
3 11
 
2.9%

father_education
Categorical

Distinct5
Distinct (%)4.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
4.0
46 
2.0
34 
5.0
28 
3.0
12 
1.0
 
4

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row2.0
3rd row2.0
4th row2.0
5th row4.0

Common Values

ValueCountFrequency (%)
4.0 46
36.8%
2.0 34
27.2%
5.0 28
22.4%
3.0 12
 
9.6%
1.0 4
 
3.2%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:25.948973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:26.030973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 46
37.1%
2.0 34
27.4%
5.0 28
22.6%
3.0 12
 
9.7%
1.0 4
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
4 46
 
12.4%
2 34
 
9.1%
5 28
 
7.5%
3 12
 
3.2%
1 4
 
1.1%

father_profession
Text

Missing 

Distinct114
Distinct (%)93.4%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
2025-04-10T23:54:26.181973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length33
Median length23.5
Mean length14.213115
Min length2

Characters and Unicode

Total characters1734
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)86.9%

Sample

1st rowprofesor
2nd rowSelf employed
3rd rowowns business
4th rowMechanic
5th rowIT
ValueCountFrequency (%)
business 11
 
4.6%
manager 8
 
3.3%
of 7
 
2.9%
owner 7
 
2.9%
engineer 7
 
2.9%
driver 5
 
2.1%
company 4
 
1.7%
construction 4
 
1.7%
salesman 4
 
1.7%
retired 3
 
1.2%
Other values (144) 180
75.0%
2025-04-10T23:54:26.442974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1734
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 180
 
10.4%
162
 
9.3%
r 132
 
7.6%
n 132
 
7.6%
i 127
 
7.3%
a 123
 
7.1%
o 107
 
6.2%
s 100
 
5.8%
t 82
 
4.7%
c 79
 
4.6%
Other values (39) 510
29.4%

fav_cuisine
Text

Missing 

Distinct60
Distinct (%)48.8%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T23:54:26.562974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length40
Median length38
Mean length9.9918699
Min length4

Characters and Unicode

Total characters1229
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique46 ?
Unique (%)37.4%

Sample

1st rowArabic cuisine
2nd rowItalian
3rd rowitalian
4th rowTurkish
5th rowItalian
ValueCountFrequency (%)
italian 56
30.8%
mexican 12
 
6.6%
chinese 11
 
6.0%
food 10
 
5.5%
american 10
 
5.5%
cuisine 7
 
3.8%
and 5
 
2.7%
thai 4
 
2.2%
indian 4
 
2.2%
or 4
 
2.2%
Other values (52) 59
32.4%
2025-04-10T23:54:26.791974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 186
15.1%
i 142
11.6%
n 140
11.4%
105
 
8.5%
e 93
 
7.6%
l 69
 
5.6%
t 67
 
5.5%
I 56
 
4.6%
c 43
 
3.5%
o 41
 
3.3%
Other values (41) 287
23.4%

fav_cuisine_coded
Real number (ℝ)

Zeros 

Distinct9
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.424
Minimum0
Maximum8
Zeros6
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:26.887973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q34
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.9479683
Coefficient of variation (CV)0.8036173
Kurtosis0.28586948
Mean2.424
Median Absolute Deviation (MAD)1
Skewness1.0211239
Sum303
Variance3.7945806
MonotonicityNot monotonic
2025-04-10T23:54:26.973979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 59
47.2%
4 22
 
17.6%
5 15
 
12.0%
2 15
 
12.0%
0 6
 
4.8%
8 4
 
3.2%
3 2
 
1.6%
6 1
 
0.8%
7 1
 
0.8%
ValueCountFrequency (%)
0 6
 
4.8%
1 59
47.2%
2 15
 
12.0%
3 2
 
1.6%
4 22
 
17.6%
5 15
 
12.0%
6 1
 
0.8%
7 1
 
0.8%
8 4
 
3.2%
ValueCountFrequency (%)
8 4
 
3.2%
7 1
 
0.8%
6 1
 
0.8%
5 15
 
12.0%
4 22
 
17.6%
3 2
 
1.6%
2 15
 
12.0%
1 59
47.2%
0 6
 
4.8%

fav_food
Categorical

Missing 

Distinct3
Distinct (%)2.4%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
1.0
73 
3.0
38 
2.0
12 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row3.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 73
58.4%
3.0 38
30.4%
2.0 12
 
9.6%
(Missing) 2
 
1.6%

Length

2025-04-10T23:54:27.062973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:27.132974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 73
59.3%
3.0 38
30.9%
2.0 12
 
9.8%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 73
19.8%
3 38
 
10.3%
2 12
 
3.3%

fries
Categorical

Imbalance 

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
114 
2
 
11

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Length

2025-04-10T23:54:27.218974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:27.296304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring characters

ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 114
91.2%
2 11
 
8.8%

fruit_day
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
63 
4
33 
3
24 
2
 
4
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row5
2nd row4
3rd row5
4th row4
5th row4

Common Values

ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Length

2025-04-10T23:54:27.384859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:27.463858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 63
50.4%
4 33
26.4%
3 24
 
19.2%
2 4
 
3.2%
1 1
 
0.8%

grade_level
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
37 
2
32 
4
28 
3
28 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Length

2025-04-10T23:54:27.550859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:27.627861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring characters

ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 37
29.6%
2 32
25.6%
4 28
22.4%
3 28
22.4%

greek_food
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
41 
3
32 
4
23 
1
15 
2
14 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Length

2025-04-10T23:54:27.713860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:27.794858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring characters

ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 41
32.8%
3 32
25.6%
4 23
18.4%
1 15
 
12.0%
2 14
 
11.2%

healthy_feeling
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.456
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:27.877860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q38
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5856427
Coefficient of variation (CV)0.47390813
Kurtosis-1.1235065
Mean5.456
Median Absolute Deviation (MAD)2
Skewness-0.058284658
Sum682
Variance6.6855484
MonotonicityNot monotonic
2025-04-10T23:54:27.953858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 17
13.6%
7 16
12.8%
5 15
12.0%
3 15
12.0%
4 13
10.4%
2 12
9.6%
6 12
9.6%
9 12
9.6%
1 8
6.4%
10 5
 
4.0%
ValueCountFrequency (%)
1 8
6.4%
2 12
9.6%
3 15
12.0%
4 13
10.4%
5 15
12.0%
6 12
9.6%
7 16
12.8%
8 17
13.6%
9 12
9.6%
10 5
 
4.0%
ValueCountFrequency (%)
10 5
 
4.0%
9 12
9.6%
8 17
13.6%
7 16
12.8%
6 12
9.6%
5 15
12.0%
4 13
10.4%
3 15
12.0%
2 12
9.6%
1 8
6.4%

ideal_diet_coded
Real number (ℝ)

Distinct8
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.704
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:28.028885image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q36
95-th percentile7
Maximum8
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0869178
Coefficient of variation (CV)0.56342273
Kurtosis-1.1654073
Mean3.704
Median Absolute Deviation (MAD)1
Skewness0.50478161
Sum463
Variance4.3552258
MonotonicityNot monotonic
2025-04-10T23:54:28.109879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 44
35.2%
3 17
 
13.6%
7 16
 
12.8%
5 15
 
12.0%
6 13
 
10.4%
1 11
 
8.8%
4 6
 
4.8%
8 3
 
2.4%
ValueCountFrequency (%)
1 11
 
8.8%
2 44
35.2%
3 17
 
13.6%
4 6
 
4.8%
5 15
 
12.0%
6 13
 
10.4%
7 16
 
12.8%
8 3
 
2.4%
ValueCountFrequency (%)
8 3
 
2.4%
7 16
 
12.8%
6 13
 
10.4%
5 15
 
12.0%
4 6
 
4.8%
3 17
 
13.6%
2 44
35.2%
1 11
 
8.8%

income
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean4.5322581
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:28.186880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q36
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4563318
Coefficient of variation (CV)0.32132587
Kurtosis-0.21082518
Mean4.5322581
Median Absolute Deviation (MAD)1
Skewness-0.82734211
Sum562
Variance2.1209022
MonotonicityNot monotonic
2025-04-10T23:54:28.262880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
6 41
32.8%
5 33
26.4%
4 20
16.0%
3 17
13.6%
2 7
 
5.6%
1 6
 
4.8%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 6
 
4.8%
2 7
 
5.6%
3 17
13.6%
4 20
16.0%
5 33
26.4%
6 41
32.8%
ValueCountFrequency (%)
6 41
32.8%
5 33
26.4%
4 20
16.0%
3 17
13.6%
2 7
 
5.6%
1 6
 
4.8%

indian_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
36 
3
31 
1
25 
2
18 
4
15 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row2

Common Values

ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Length

2025-04-10T23:54:28.351880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:28.436880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring characters

ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 36
28.8%
3 31
24.8%
1 25
20.0%
2 18
14.4%
4 15
12.0%

italian_food
Categorical

Distinct3
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
100 
4
16 
3
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Length

2025-04-10T23:54:28.535879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:28.613879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring characters

ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 100
80.0%
4 16
 
12.8%
3 9
 
7.2%

life_rewarding
Real number (ℝ)

High correlation 

Distinct10
Distinct (%)8.1%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean5.1048387
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:28.688883image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.1203985
Coefficient of variation (CV)0.6112629
Kurtosis-1.4751391
Mean5.1048387
Median Absolute Deviation (MAD)3
Skewness0.062603108
Sum633
Variance9.736887
MonotonicityNot monotonic
2025-04-10T23:54:28.763880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 23
18.4%
8 18
14.4%
3 15
12.0%
7 14
11.2%
2 13
10.4%
9 11
8.8%
10 10
8.0%
5 10
8.0%
4 6
 
4.8%
6 4
 
3.2%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 23
18.4%
2 13
10.4%
3 15
12.0%
4 6
 
4.8%
5 10
8.0%
6 4
 
3.2%
7 14
11.2%
8 18
14.4%
9 11
8.8%
10 10
8.0%
ValueCountFrequency (%)
10 10
8.0%
9 11
8.8%
8 18
14.4%
7 14
11.2%
6 4
 
3.2%
5 10
8.0%
4 6
 
4.8%
3 15
12.0%
2 13
10.4%
1 23
18.4%

marital_status
Categorical

Distinct3
Distinct (%)2.4%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
64 
2.0
59 
4.0
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 64
51.2%
2.0 59
47.2%
4.0 1
 
0.8%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:28.847881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:28.921881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 64
51.6%
2.0 59
47.6%
4.0 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 64
17.2%
2 59
15.9%
4 1
 
0.3%

mother_education
Categorical

Missing 

Distinct5
Distinct (%)4.1%
Missing3
Missing (%)2.4%
Memory size1.1 KiB
4.0
46 
2.0
30 
5.0
23 
3.0
18 
1.0

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row4.0
5th row5.0

Common Values

ValueCountFrequency (%)
4.0 46
36.8%
2.0 30
24.0%
5.0 23
18.4%
3.0 18
 
14.4%
1.0 5
 
4.0%
(Missing) 3
 
2.4%

Length

2025-04-10T23:54:29.002879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:29.081878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4.0 46
37.7%
2.0 30
24.6%
5.0 23
18.9%
3.0 18
 
14.8%
1.0 5
 
4.1%

Most occurring characters

ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 366
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 122
33.3%
0 122
33.3%
4 46
 
12.6%
2 30
 
8.2%
5 23
 
6.3%
3 18
 
4.9%
1 5
 
1.4%

mother_profession
Text

Missing 

Distinct112
Distinct (%)91.1%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
2025-04-10T23:54:29.233881image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length48
Median length31
Mean length14.813008
Min length2

Characters and Unicode

Total characters1822
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique103 ?
Unique (%)83.7%

Sample

1st rowunemployed
2nd rowNurse RN
3rd rowowns business
4th rowSpecial Education Teacher
5th rowSubstance Abuse Conselor
ValueCountFrequency (%)
teacher 14
 
5.6%
secretary 7
 
2.8%
business 6
 
2.4%
nurse 5
 
2.0%
school 5
 
2.0%
unemployed 4
 
1.6%
home 4
 
1.6%
accountant 4
 
1.6%
in 4
 
1.6%
coordinator 3
 
1.2%
Other values (151) 192
77.4%
2025-04-10T23:54:29.520628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1822
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 209
 
11.5%
156
 
8.6%
r 154
 
8.5%
a 150
 
8.2%
t 115
 
6.3%
o 106
 
5.8%
i 106
 
5.8%
n 94
 
5.2%
s 93
 
5.1%
c 86
 
4.7%
Other values (38) 553
30.4%
Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
4
43 
2
36 
3
20 
5
16 
1
10 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row4
4th row2
5th row3

Common Values

ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Length

2025-04-10T23:54:29.629138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:29.708139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring characters

ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 43
34.4%
2 36
28.8%
3 20
16.0%
5 16
 
12.8%
1 10
 
8.0%

on_off_campus
Categorical

Distinct4
Distinct (%)3.2%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
97 
2.0
16 
3.0
 
9
4.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 97
77.6%
2.0 16
 
12.8%
3.0 9
 
7.2%
4.0 2
 
1.6%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:29.794138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:29.868138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 97
78.2%
2.0 16
 
12.9%
3.0 9
 
7.3%
4.0 2
 
1.6%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 16
 
4.3%
3 9
 
2.4%
4 2
 
0.5%

parents_cook
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1
75 
2
36 
3
13 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Length

2025-04-10T23:54:29.956139image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:30.032138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 75
60.0%
2 36
28.8%
3 13
 
10.4%
5 1
 
0.8%

pay_meal_out
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
3
67 
4
22 
2
17 
5
11 
6

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row3
4th row2
5th row4

Common Values

ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Length

2025-04-10T23:54:30.444167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:30.531168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring characters

ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 67
53.6%
4 22
 
17.6%
2 17
 
13.6%
5 11
 
8.8%
6 8
 
6.4%

persian_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
30 
3.0
29 
2.0
26 
5.0
23 
4.0
16 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5.0
2nd row4.0
3rd row5.0
4th row5.0
5th row2.0

Common Values

ValueCountFrequency (%)
1.0 30
24.0%
3.0 29
23.2%
2.0 26
20.8%
5.0 23
18.4%
4.0 16
12.8%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:30.619168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:30.699167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 30
24.2%
3.0 29
23.4%
2.0 26
21.0%
5.0 23
18.5%
4.0 16
12.9%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 30
 
8.1%
3 29
 
7.8%
2 26
 
7.0%
5 23
 
6.2%
4 16
 
4.3%

self_perception_weight
Real number (ℝ)

Distinct6
Distinct (%)4.8%
Missing1
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean3.1209677
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:30.779167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median3
Q34
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1159796
Coefficient of variation (CV)0.35757485
Kurtosis0.2673123
Mean3.1209677
Median Absolute Deviation (MAD)1
Skewness0.47081775
Sum387
Variance1.2454104
MonotonicityNot monotonic
2025-04-10T23:54:30.855166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 45
36.0%
4 31
24.8%
2 31
24.8%
5 6
 
4.8%
1 6
 
4.8%
6 5
 
4.0%
(Missing) 1
 
0.8%
ValueCountFrequency (%)
1 6
 
4.8%
2 31
24.8%
3 45
36.0%
4 31
24.8%
5 6
 
4.8%
6 5
 
4.0%
ValueCountFrequency (%)
6 5
 
4.0%
5 6
 
4.8%
4 31
24.8%
3 45
36.0%
2 31
24.8%
1 6
 
4.8%

soup
Categorical

Distinct2
Distinct (%)1.6%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1.0
97 
2.0
27 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters372
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 97
77.6%
2.0 27
 
21.6%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:30.941168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:31.012167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 97
78.2%
2.0 27
 
21.8%

Most occurring characters

ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 372
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 124
33.3%
0 124
33.3%
1 97
26.1%
2 27
 
7.3%

sports
Categorical

Missing 

Distinct2
Distinct (%)1.6%
Missing2
Missing (%)1.6%
Memory size1.1 KiB
1.0
75 
2.0
48 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 75
60.0%
2.0 48
38.4%
(Missing) 2
 
1.6%

Length

2025-04-10T23:54:31.090167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:31.163168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 75
61.0%
2.0 48
39.0%

Most occurring characters

ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 369
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 123
33.3%
0 123
33.3%
1 75
20.3%
2 48
 
13.0%

thai_food
Categorical

High correlation 

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
37 
3
26 
4
25 
1
20 
2
17 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row5
4th row5
5th row4

Common Values

ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Length

2025-04-10T23:54:31.241167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:31.322166image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring characters

ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 37
29.6%
3 26
20.8%
4 25
20.0%
1 20
16.0%
2 17
13.6%
Distinct4
Distinct (%)3.2%
Missing1
Missing (%)0.8%
Memory size1.1 KiB
1165.0
46 
940.0
43 
725.0
22 
580.0
13 

Length

Max length6
Median length5
Mean length5.3709677
Min length5

Characters and Unicode

Total characters666
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1165.0
2nd row725.0
3rd row1165.0
4th row725.0
5th row940.0

Common Values

ValueCountFrequency (%)
1165.0 46
36.8%
940.0 43
34.4%
725.0 22
17.6%
580.0 13
 
10.4%
(Missing) 1
 
0.8%

Length

2025-04-10T23:54:31.413167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:31.492167image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1165.0 46
37.1%
940.0 43
34.7%
725.0 22
17.7%
580.0 13
 
10.5%

Most occurring characters

ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 666
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 180
27.0%
. 124
18.6%
1 92
13.8%
5 81
12.2%
6 46
 
6.9%
9 43
 
6.5%
4 43
 
6.5%
7 22
 
3.3%
2 22
 
3.3%
8 13
 
2.0%

turkey_calories
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
500
50 
690
39 
345
26 
850
10 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters375
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row345
2nd row690
3rd row500
4th row690
5th row500

Common Values

ValueCountFrequency (%)
500 50
40.0%
690 39
31.2%
345 26
20.8%
850 10
 
8.0%

Length

2025-04-10T23:54:31.579168image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:31.654246image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
500 50
40.0%
690 39
31.2%
345 26
20.8%
850 10
 
8.0%

Most occurring characters

ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 375
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 149
39.7%
5 86
22.9%
6 39
 
10.4%
9 39
 
10.4%
3 26
 
6.9%
4 26
 
6.9%
8 10
 
2.7%
Distinct67
Distinct (%)53.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:31.765464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length59
Median length38
Mean length10.344
Min length4

Characters and Unicode

Total characters1293
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)40.8%

Sample

1st rowcar racing
2nd rowBasketball
3rd rownone
4th rowUnknow
5th rowSoftball
ValueCountFrequency (%)
unknow 26
 
13.4%
hockey 15
 
7.7%
none 14
 
7.2%
soccer 11
 
5.7%
softball 10
 
5.2%
basketball 10
 
5.2%
volleyball 9
 
4.6%
and 5
 
2.6%
tennis 5
 
2.6%
lacrosse 5
 
2.6%
Other values (59) 84
43.3%
2025-04-10T23:54:31.991471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1293
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 139
 
10.8%
o 123
 
9.5%
l 109
 
8.4%
e 104
 
8.0%
89
 
6.9%
a 85
 
6.6%
c 62
 
4.8%
k 56
 
4.3%
s 52
 
4.0%
t 49
 
3.8%
Other values (37) 425
32.9%

veggies_day
Categorical

Distinct5
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
5
53 
4
37 
3
21 
2
11 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row4
3rd row5
4th row3
5th row4

Common Values

ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Length

2025-04-10T23:54:32.091469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:32.169471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5 53
42.4%
4 37
29.6%
3 21
 
16.8%
2 11
 
8.8%
1 3
 
2.4%

vitamins
Categorical

Distinct2
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2
64 
1
61 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters125
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Length

2025-04-10T23:54:32.260470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:32.332469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring characters

ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 64
51.2%
1 61
48.8%

waffle_calories
Categorical

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
1315
62 
900
38 
760
22 
575
 
3

Length

Max length4
Median length3
Mean length3.496
Min length3

Characters and Unicode

Total characters437
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1315
2nd row900
3rd row900
4th row1315
5th row760

Common Values

ValueCountFrequency (%)
1315 62
49.6%
900 38
30.4%
760 22
 
17.6%
575 3
 
2.4%

Length

2025-04-10T23:54:32.411471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-10T23:54:32.489470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1315 62
49.6%
900 38
30.4%
760 22
 
17.6%
575 3
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 437
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 124
28.4%
0 98
22.4%
5 68
15.6%
3 62
14.2%
9 38
 
8.7%
7 25
 
5.7%
6 22
 
5.0%

weight
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159.17355
Minimum100
Maximum265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2025-04-10T23:54:32.580472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile115.2
Q1135
median156
Q3180
95-th percentile209
Maximum265
Range165
Interquartile range (IQR)45

Descriptive statistics

Standard deviation31.953766
Coefficient of variation (CV)0.20074796
Kurtosis1.2886785
Mean159.17355
Median Absolute Deviation (MAD)21
Skewness0.8613772
Sum19896.694
Variance1021.0432
MonotonicityNot monotonic
2025-04-10T23:54:32.680470image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
140 8
 
6.4%
135 8
 
6.4%
150 7
 
5.6%
170 7
 
5.6%
185 6
 
4.8%
175 6
 
4.8%
155 6
 
4.8%
180 6
 
4.8%
125 5
 
4.0%
190 5
 
4.0%
Other values (36) 61
48.8%
ValueCountFrequency (%)
100 1
 
0.8%
105 1
 
0.8%
110 1
 
0.8%
112 1
 
0.8%
113 2
1.6%
115 1
 
0.8%
116 1
 
0.8%
118 1
 
0.8%
120 3
2.4%
123 1
 
0.8%
ValueCountFrequency (%)
265 1
 
0.8%
264 1
 
0.8%
260 1
 
0.8%
240 1
 
0.8%
230 1
 
0.8%
210 2
1.6%
205 1
 
0.8%
200 4
3.2%
195 1
 
0.8%
192 1
 
0.8%

Interactions

2025-04-10T23:54:20.068980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.094689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.984206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.735206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.560487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.276487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.048489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.908948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.673946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.402948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.138981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.139980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.169201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.056206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.800209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.630487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.352488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.117488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.984946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.744947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.481947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.209981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.210983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.245205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.125205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.870205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.702489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.426492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.186491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.061947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.817948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.554947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.278980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.270981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.312205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.188211image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.931206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.764492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.493488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.247492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.129947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.879947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.628460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.340980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.333980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.511210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.255205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.993206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.827492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.564492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.305489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.195946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.938947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.693972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.404981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.403986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.580210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.328210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.062205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.896488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.641487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.371490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.268946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.008947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.764984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.472980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.462980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.644206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.391205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.122205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.958492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.706487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.429428image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.331948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.070948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.822986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.531981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.532981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.717210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.461209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.187207image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.025488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.780488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.494947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.404948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.139946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.894984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.599980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.594981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.784206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.527206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.247979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.087490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.849487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.555949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.470947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.204948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.955984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.657980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.655980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.849206image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.593210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.428493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.146487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.911489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.619948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.535946image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.262948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.011981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.718980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:20.724981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:12.916209image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:13.661210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:14.492488image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.210490image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:15.979487image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:16.841949image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:17.600948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:18.326947image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.073980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-04-10T23:54:19.998980image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-04-10T23:54:32.784471image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
GPAGenderbreakfastcalories_daycalories_sconecoffeecomfort_food_reasons_coded.1cookcuisinediet_current_codeddrinkeating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfav_cuisine_codedfav_foodfriesfruit_daygrade_levelgreek_foodhealthy_feelingideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmother_educationnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriesveggies_dayvitaminswaffle_caloriesweight
GPA1.0000.0000.0000.0000.1650.000-0.0690.000-0.0670.1150.1620.1260.0810.0840.0000.2200.0690.213-0.0280.0000.1910.3070.0710.102-0.077-0.1510.0830.0660.170-0.0050.3640.2380.2070.1380.0000.0770.0000.0200.0000.0000.1530.1510.1080.1730.0000.000-0.051
Gender0.0001.0000.0540.0000.1180.0000.1510.3290.0000.0640.1640.0770.0000.0650.1250.1400.1540.0000.1560.0780.0000.0640.0000.1600.2110.2900.0000.0000.0000.0000.1910.2050.1200.0690.0680.1150.0000.2400.0000.1550.1470.1630.2820.1000.0000.0000.502
breakfast0.0000.0541.0000.0000.0000.0000.1760.1000.0000.1860.1660.1230.1060.0000.0000.3220.0000.1900.2410.1900.0000.2990.1280.2580.0000.0000.1610.1900.0000.1100.2190.0000.0810.1090.0000.0000.0000.0000.1220.0000.0000.0000.0330.2950.0000.0000.000
calories_day0.0000.0000.0001.0000.1260.0280.1470.0550.0000.1000.0000.0000.1640.0670.0000.1590.2000.0190.0000.0780.0000.1640.0000.0990.1530.2370.0000.0970.0000.0000.0000.0000.2690.0000.0000.0000.0000.1510.0000.0790.1350.1170.1680.1070.3090.1300.000
calories_scone0.1650.1180.0000.1261.0000.0560.2270.1190.1740.0750.0400.0000.0000.1540.1050.0900.0000.2770.2080.0000.0000.0430.0000.0000.1560.0000.0170.1260.1320.1240.0000.0000.2620.1150.1870.1430.0000.2810.0000.0000.1110.1810.1030.1230.0000.2740.000
coffee0.0000.0000.0000.0280.0561.0000.1570.0990.0000.0000.0000.0000.1700.1330.0000.1470.0000.0000.2110.0000.0000.0000.1150.0000.0000.1410.0000.0000.0960.1300.1330.1260.2260.0000.0800.0000.0000.0000.1440.0000.1060.0000.0000.0770.0000.0000.088
comfort_food_reasons_coded.1-0.0690.1510.1760.1470.2270.1571.0000.1320.1430.1720.1640.000-0.0310.0000.0810.0000.1620.157-0.0210.1250.1350.1290.0490.000-0.0640.042-0.0800.0580.0000.0440.2660.2090.1820.0000.0000.0000.173-0.0900.1280.0830.0000.0210.0560.1750.0000.000-0.072
cook0.0000.3290.1000.0550.1190.0990.1321.0000.0000.1680.1040.0000.0000.0340.0000.0920.1610.1370.0000.0000.1590.0000.1430.0000.0000.1700.0000.0000.1260.0730.0000.0790.1660.0790.1540.0920.0000.0000.0000.1380.0370.1060.1700.0000.0550.2230.145
cuisine-0.0670.0000.0000.0000.1740.0000.1430.0001.0000.0870.1330.0000.1100.0000.0000.0000.0730.1790.0010.1350.2280.0000.0380.0000.1330.088-0.1040.0760.2080.0980.3840.1900.0000.0000.0620.1440.1020.1380.0000.0990.0660.0780.0000.0960.0000.2430.125
diet_current_coded0.1150.0640.1860.1000.0750.0000.1720.1680.0871.0000.3050.1980.2580.0300.0000.0000.0780.0450.0000.0000.1540.1030.0000.0000.1000.1260.2130.0000.1100.1090.2950.0000.1970.1670.0000.0010.0320.0000.0000.0120.0470.0730.0000.2160.1380.1570.114
drink0.1620.1640.1660.0000.0400.0000.1640.1040.1330.3051.0000.1890.1310.0000.1150.1290.0000.0000.0000.0220.1910.0810.0000.0610.0000.0000.2430.1720.0000.1160.0000.1630.2090.0000.0000.0140.2700.1010.0000.0000.0000.0000.0000.2520.1260.0000.192
eating_changes_coded0.1260.0770.1230.0000.0000.0000.0000.0000.0000.1980.1891.0000.7840.0000.1590.1080.0000.1370.0000.0000.0000.0000.0000.0740.0000.0000.0000.1330.1780.0000.1810.0990.1520.1230.0000.1000.0000.0000.0000.0000.0680.1210.0000.0000.0860.0000.080
eating_changes_coded10.0810.0000.1060.1640.0000.170-0.0310.0000.1100.2580.1310.7841.0000.0800.0550.0730.0000.0000.0940.0000.1210.0720.0000.037-0.030-0.021-0.0090.1380.0420.0180.0000.0880.1360.0230.0000.0220.119-0.0430.1960.0000.0670.0000.0000.0000.1830.0000.014
eating_out0.0840.0650.0000.0670.1540.1330.0000.0340.0000.0300.0000.0000.0801.0000.0000.0000.0000.2270.0270.0940.0790.0000.1030.0000.1090.0000.0000.0000.0000.0900.1120.0970.0000.0000.1610.0560.1150.0000.0000.0000.0000.0000.0930.0000.1160.1160.167
employment0.0000.1250.0000.0000.1050.0000.0810.0000.0000.0000.1150.1590.0550.0001.0000.0000.0000.1140.0000.0000.0420.0000.0000.0520.0000.0600.1150.0000.0000.0000.0000.0980.0730.3430.0000.0000.0000.1700.0000.1410.1030.1050.0000.0000.0000.0000.070
ethnic_food0.2200.1400.3220.1590.0900.1470.0000.0920.0000.0000.1290.1080.0730.0000.0001.0000.0000.0890.2560.2440.0350.2580.0000.3320.0000.0000.0000.4640.1700.1430.0000.0000.1540.0000.0730.1050.3650.0000.2640.0000.3950.0000.0490.1660.0660.0570.000
exercise0.0690.1540.0000.2000.0000.0000.1620.1610.0730.0780.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.2010.0700.0000.2020.0000.0000.0000.1070.0000.0000.0000.1190.1190.0000.0000.0000.3080.0000.4180.0000.0750.0690.1030.0000.0000.225
father_education0.2130.0000.1900.0190.2770.0000.1570.1370.1790.0450.0000.1370.0000.2270.1140.0890.0001.0000.3260.0260.0360.1590.0810.1060.1440.0900.2740.1340.2560.1540.3210.3060.1020.1390.0920.1860.1390.0000.0000.1140.0000.1200.0830.1350.1710.0850.000
fav_cuisine_coded-0.0280.1560.2410.0000.2080.211-0.0210.0000.0010.0000.0000.0000.0940.0270.0000.2560.0000.3261.0000.1150.3430.1880.0000.160-0.043-0.046-0.2860.2060.3490.0310.2090.2460.0340.0000.1670.2230.1820.0550.0000.0000.2110.1620.0000.2490.1010.1250.039
fav_food0.0000.0780.1900.0780.0000.0000.1250.0000.1350.0000.0220.0000.0000.0940.0000.2440.0000.0260.1151.0000.0850.1190.0000.2540.0000.0000.0000.1270.0930.0610.1500.1690.0000.0810.1090.1390.0760.0890.2530.0630.0950.0000.1310.1270.0000.0000.000
fries0.1910.0000.0000.0000.0000.0000.1350.1590.2280.1540.1910.0000.1210.0790.0420.0350.0000.0360.3430.0851.0000.0000.0550.1020.0000.0000.0000.1520.0000.1520.0000.0000.0000.0000.0000.1070.0000.0000.0000.0000.0000.0000.0000.0000.2770.1420.258
fruit_day0.3070.0640.2990.1640.0430.0000.1290.0000.0000.1030.0810.0000.0720.0000.0000.2580.2010.1590.1880.1190.0001.0000.0970.1260.1710.0000.0000.1240.0000.1110.0000.0790.1460.0000.0000.0000.0140.1650.1650.0000.0240.0000.0790.4260.2470.0000.000
grade_level0.0710.0000.1280.0000.0000.1150.0490.1430.0380.0000.0000.0000.0000.1030.0000.0000.0700.0810.0000.0000.0550.0971.0000.1340.0000.0800.1710.0450.1650.0000.0580.0910.1360.2610.0950.0000.1070.1400.0000.2150.2130.0000.0330.0670.0000.0000.179
greek_food0.1020.1600.2580.0990.0000.0000.0000.0000.0000.0000.0610.0740.0370.0000.0520.3320.0000.1060.1600.2540.1020.1260.1341.0000.0000.0000.0000.3670.1650.0000.0000.1050.1080.0000.0000.0000.4140.0000.0000.0340.3210.0000.0000.1030.0000.0800.000
healthy_feeling-0.0770.2110.0000.1530.1560.000-0.0640.0000.1330.1000.0000.000-0.0300.1090.0000.0000.2020.144-0.0430.0000.0000.1710.0000.0001.000-0.0340.0140.0000.0000.6260.0000.0000.1630.0000.1450.1020.0000.0160.0000.2980.0710.0000.0000.1620.0520.0000.048
ideal_diet_coded-0.1510.2900.0000.2370.0000.1410.0420.1700.0880.1260.0000.000-0.0210.0000.0600.0000.0000.090-0.0460.0000.0000.0000.0800.000-0.0341.0000.0820.0000.000-0.0040.3740.2370.0460.0000.0810.0000.000-0.2830.0000.1600.0000.0000.0630.1630.0000.0000.096
income0.0830.0000.1610.0000.0170.000-0.0800.000-0.1040.2130.2430.000-0.0090.0000.1150.0000.0000.274-0.2860.0000.0000.0000.1710.0000.0140.0821.0000.0520.0850.0380.0000.1860.0720.0000.0000.0310.000-0.1470.1510.1480.0000.0000.1060.0300.0000.1220.158
indian_food0.0660.0000.1900.0970.1260.0000.0580.0000.0760.0000.1720.1330.1380.0000.0000.4640.0000.1340.2060.1270.1520.1240.0450.3670.0000.0000.0521.0000.0300.1070.0000.1510.0000.0940.0000.0000.5470.0000.0670.0000.5570.0000.0000.0000.0000.1260.000
italian_food0.1700.0000.0000.0000.1320.0960.0000.1260.2080.1100.0000.1780.0420.0000.0000.1700.1070.2560.3490.0930.0000.0000.1650.1650.0000.0000.0850.0301.0000.0680.2720.2270.0000.0000.0000.0000.0590.1790.0800.2300.1640.1430.0000.1000.1700.1390.000
life_rewarding-0.0050.0000.1100.0000.1240.1300.0440.0730.0980.1090.1160.0000.0180.0900.0000.1430.0000.1540.0310.0610.1520.1110.0000.0000.626-0.0040.0380.1070.0681.0000.2180.0520.0880.0000.0000.0000.104-0.1280.0000.0000.0000.0000.0000.1400.0000.061-0.077
marital_status0.3640.1910.2190.0000.0000.1330.2660.0000.3840.2950.0000.1810.0000.1120.0000.0000.0000.3210.2090.1500.0000.0000.0580.0000.0000.3740.0000.0000.2720.2181.0000.3020.0000.0620.1700.1500.0000.0000.1140.0770.0000.1310.0000.0290.1820.0410.000
mother_education0.2380.2050.0000.0000.0000.1260.2090.0790.1900.0000.1630.0990.0880.0970.0980.0000.0000.3060.2460.1690.0000.0790.0910.1050.0000.2370.1860.1510.2270.0520.3021.0000.0000.0000.0000.1230.1760.0000.0000.0000.0000.0000.0860.0790.0000.0000.019
nutritional_check0.2070.1200.0810.2690.2620.2260.1820.1660.0000.1970.2090.1520.1360.0000.0730.1540.1190.1020.0340.0000.0000.1460.1360.1080.1630.0460.0720.0000.0000.0880.0000.0001.0000.0000.0000.1040.0000.0000.0000.0000.0000.0000.0000.2060.3740.0000.000
on_off_campus0.1380.0690.1090.0000.1150.0000.0000.0790.0000.1670.0000.1230.0230.0000.3430.0000.1190.1390.0000.0810.0000.0000.2610.0000.0000.0000.0000.0940.0000.0000.0620.0000.0001.0000.0680.0000.0710.0560.0000.0000.0630.0000.0920.0000.0000.0000.094
parents_cook0.0000.0680.0000.0000.1870.0800.0000.1540.0620.0000.0000.0000.0000.1610.0000.0730.0000.0920.1670.1090.0000.0000.0950.0000.1450.0810.0000.0000.0000.0000.1700.0000.0000.0681.0000.1170.0000.0000.0000.1640.0000.1590.0390.0000.0000.0440.000
pay_meal_out0.0770.1150.0000.0000.1430.0000.0000.0920.1440.0010.0140.1000.0220.0560.0000.1050.0000.1860.2230.1390.1070.0000.0000.0000.1020.0000.0310.0000.0000.0000.1500.1230.1040.0000.1171.0000.0000.0000.0000.0000.0950.0000.0000.1430.1930.0000.159
persian_food0.0000.0000.0000.0000.0000.0000.1730.0000.1020.0320.2700.0000.1190.1150.0000.3650.0000.1390.1820.0760.0000.0140.1070.4140.0000.0000.0000.5470.0590.1040.0000.1760.0000.0710.0000.0001.0000.0000.1310.0000.3950.1070.0000.0000.0000.0000.147
self_perception_weight0.0200.2400.0000.1510.2810.000-0.0900.0000.1380.0000.1010.000-0.0430.0000.1700.0000.3080.0000.0550.0890.0000.1650.1400.0000.016-0.283-0.1470.0000.179-0.1280.0000.0000.0000.0560.0000.0000.0001.0000.0000.2970.0000.0730.0000.0960.0000.0730.207
soup0.0000.0000.1220.0000.0000.1440.1280.0000.0000.0000.0000.0000.1960.0000.0000.2640.0000.0000.0000.2530.0000.1650.0000.0000.0000.0000.1510.0670.0800.0000.1140.0000.0000.0000.0000.0000.1310.0001.0000.0000.0000.0990.0000.1170.0000.0000.181
sports0.0000.1550.0000.0790.0000.0000.0830.1380.0990.0120.0000.0000.0000.0000.1410.0000.4180.1140.0000.0630.0000.0000.2150.0340.2980.1600.1480.0000.2300.0000.0770.0000.0000.0000.1640.0000.0000.2970.0001.0000.0790.1430.1460.0000.0000.0000.271
thai_food0.1530.1470.0000.1350.1110.1060.0000.0370.0660.0470.0000.0680.0670.0000.1030.3950.0000.0000.2110.0950.0000.0240.2130.3210.0710.0000.0000.5570.1640.0000.0000.0000.0000.0630.0000.0950.3950.0000.0000.0791.0000.0770.0680.0000.0000.0300.189
tortilla_calories0.1510.1630.0000.1170.1810.0000.0210.1060.0780.0730.0000.1210.0000.0000.1050.0000.0750.1200.1620.0000.0000.0000.0000.0000.0000.0000.0000.0000.1430.0000.1310.0000.0000.0000.1590.0000.1070.0730.0990.1430.0771.0000.2550.0930.0000.2280.082
turkey_calories0.1080.2820.0330.1680.1030.0000.0560.1700.0000.0000.0000.0000.0000.0930.0000.0490.0690.0830.0000.1310.0000.0790.0330.0000.0000.0630.1060.0000.0000.0000.0000.0860.0000.0920.0390.0000.0000.0000.0000.1460.0680.2551.0000.0790.0000.2130.040
veggies_day0.1730.1000.2950.1070.1230.0770.1750.0000.0960.2160.2520.0000.0000.0000.0000.1660.1030.1350.2490.1270.0000.4260.0670.1030.1620.1630.0300.0000.1000.1400.0290.0790.2060.0000.0000.1430.0000.0960.1170.0000.0000.0930.0791.0000.2330.1020.000
vitamins0.0000.0000.0000.3090.0000.0000.0000.0550.0000.1380.1260.0860.1830.1160.0000.0660.0000.1710.1010.0000.2770.2470.0000.0000.0520.0000.0000.0000.1700.0000.1820.0000.3740.0000.0000.1930.0000.0000.0000.0000.0000.0000.0000.2331.0000.1650.000
waffle_calories0.0000.0000.0000.1300.2740.0000.0000.2230.2430.1570.0000.0000.0000.1160.0000.0570.0000.0850.1250.0000.1420.0000.0000.0800.0000.0000.1220.1260.1390.0610.0410.0000.0000.0000.0440.0000.0000.0730.0000.0000.0300.2280.2130.1020.1651.0000.119
weight-0.0510.5020.0000.0000.0000.088-0.0720.1450.1250.1140.1920.0800.0140.1670.0700.0000.2250.0000.0390.0000.2580.0000.1790.0000.0480.0960.1580.0000.000-0.0770.0000.0190.0000.0940.0000.1590.1470.2070.1810.2710.1890.0820.0400.0000.0000.1191.000

Missing values

2025-04-10T23:54:20.884298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-10T23:54:21.125240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-04-10T23:54:21.433239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

GPAGenderbreakfastcalories_daycalories_sconecoffeecomfort_foodcomfort_food_reasonscookcomfort_food_reasons_coded.1cuisinediet_current_codeddrinkeating_changeseating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfather_professionfav_cuisinefav_cuisine_codedfav_foodfriesfruit_daygrade_levelgreek_foodhealthy_feelingideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmother_educationmother_professionnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriestype_sportsveggies_dayvitaminswaffle_caloriesweight
02.400210.0315.01nonewe dont have comfort2.096.011.0eat faster1133.011.05.0profesorArabic cuisine31.02525285.0551.01.01.0unemployed51.0125.03.01.01.011165.0345car racing511315187.000000
13.654113.0420.02chocolate, chips, ice creamStress, bored, anger3.011.022.0I eat out more than usual.1222.041.02.0Self employedItalian11.01444534.0441.02.04.0Nurse RN41.0144.03.01.01.02725.0690Basketball42900155.000000
23.300114.0420.02frozen yogurt, pizza, fast foodstress, sadness1.013.031.0sometimes choosing to eat fast food instead of cooking simply for convenience1323.052.02.0owns businessitalian13.01535666.0557.02.02.0owns business42.0135.06.01.02.051165.0500none51900159.173554
33.200113.0420.02Pizza, Mac and cheese, ice creamBoredom2.022.022.0Accepting cheap and premade/store bought foods1323.053.02.0MechanicTurkish31.02445726.0552.02.04.0Special Education Teacher21.0125.05.01.02.05725.0690Unknow311315240.000000
43.500112.0420.02Ice cream, chocolate, chipsStress, boredom, cravings1.012.022.0I have eaten generally the same foods but I do find myself eating the same food frequently due to what I have found I like from egan and the laker.3422.041.04.0ITItalian13.01444626.0251.01.05.0Substance Abuse Conselor31.0142.04.01.01.04940.0500Softball42760190.000000
52.250113.0980.02Candy, brownies and soda.None, i don't eat comfort food. I just eat when i'm hungry.3.046.022.0Eating rice everyday. Eating less homemade food.1313.042.01.0Taxi DriverAfrican63.01222421.0554.02.01.0Hair Braider11.0255.05.01.02.04940.0345None.121315190.000000
63.800213.0420.02Chocolate, ice cream, french fries, pretzelsstress, boredom2.011.031.0I started eating a lot less and healthier because I wasn't playing sports year round anymore.2523.051.04.0AssemblerThai41.01445424.0558.01.04.0Journalist42.0225.04.01.01.05940.0690soccer411315180.000000
73.300113.0420.01Ice cream, cheeseburgers, chips.I eat comfort food when im stressed out from school(finals week), when I`m sad, or when i am dealing with personal family issues.3.011.012.0Freshmen year i ate very unhealthy, but now it is much healthier because of self control.2522.022.03.0Business guyAnything american style.51.01523325.0133.01.02.0cook41.0151.03.01.02.01725.0500none421315137.000000
83.300110.0420.01Donuts, ice cream, chipsBoredom3.021.011.0I snack less2852.050.05.0High School PrincipalSeafood13.01415765.0558.02.05.0Elementary School Teacher21.0235.04.02.02.05725.0345none32760180.000000
93.300113.0315.02Mac and cheese, chocolate, and pastaStress, anger and sadness3.011.011.0I cook a lot of my own foods back at home so not being able to cook my own healthy choices. I eat more carbs than normal when I'm at college due to the choices given in the cafe.1333.051.05.0commissioner of erie countyItalian11.01515324.0453.02.05.0Pharmaceutical rep51.0334.03.01.01.04580.0345field hockey51900125.000000
GPAGenderbreakfastcalories_daycalories_sconecoffeecomfort_foodcomfort_food_reasonscookcomfort_food_reasons_coded.1cuisinediet_current_codeddrinkeating_changeseating_changes_codedeating_changes_coded1eating_outemploymentethnic_foodexercisefather_educationfather_professionfav_cuisinefav_cuisine_codedfav_foodfriesfruit_daygrade_levelgreek_foodhealthy_feelingideal_diet_codedincomeindian_fooditalian_foodlife_rewardingmarital_statusmother_educationmother_professionnutritional_checkon_off_campusparents_cookpay_meal_outpersian_foodself_perception_weightsoupsportsthai_foodtortilla_caloriesturkey_caloriestype_sportsveggies_dayvitaminswaffle_caloriesweight
1153.300214.0980.02chocolate bar, ice cream, pretzels, potato chips and protein bars.Stress, boredom and physical activity4.011.022.0Eating more dairy due to more ice cream intake.1322.021.05.0PharmaceuticalI do not like cuisine03.015111036.01410.02.05.0Health teacher11.0152.02.01.01.011165.0690Hockey221315150.0
1163.400110.0420.02Ice cream, chocolate, pizza, cucumberloneliness, homework, boredom3.026.021.0less vegetable more sweats1343.042.03.0Business ManChinese42.01511551.0352.01.03.0Business Woman21.0231.04.01.02.05725.0345none511315170.0
1173.770110.0315.02Noodle ( any kinds of noodle), Tuna sandwich, and Egg.\rWhen i'm eating with my close friends/ Food smell or look good/ when I feel tired3.054.011.0I eat more vegetable. Since coming to college, I started to eat salads and tried to eat salads at least three times a week.2522.040.02.0His own businessVietnamese cuisine43.01322922.0247.01.02.0Her own business21.0122.04.01.02.05725.0690No, I don't play sport.31760113.0
1183.630113.0420.01Chinese, chips, cakeStress and boredom3.011.021.0I try to eat more fruits and vegetables2522.042.02.0HVAC technicianAmerican51.01523553.0358.02.02.0Grieveance coordinator of the SCI albion prison23.0342.04.01.02.04940.0345Unknow521315140.0
1193.200213.0420.02chips, rice, chicken curry,Happiness, boredom, social event2.075.021.0Started eating a lot of protein rich food that i didnt before.2522.052.05.0United NationsIndian83.01545732.0556.02.05.0Banker21.0135.04.01.01.051165.0690Soccer521315185.0
1203.500114.0420.02wine. mac and cheese, pizza, ice creamboredom and sadness3.021.022.0I have noticed there is less time for a prepared meal, so quick and easy has become the norm.1321.042.04.0AccountantItalian11.01545564.0357.01.03.0Radiological Technician53.0143.04.01.01.05940.0500Softball511315156.0
1213.000112.0315.02Pizza / Wings / CheesecakeLoneliness / Homesick / Sadness3.036.021.0Eating Pizza as an excuse when there is nothing else to it.1343.032.05.0DoctorMexican Food21.01441552.0557.01.02.0Public Health Advisor31.0341.04.01.0NaN4940.0500basketball521315180.0
1223.882110.0420.01rice, potato, seaweed soupsadness3.036.021.0less rice1333.052.05.0CEO of companyKorean41.01435622.05310.01.01.0Real Estate manageer31.0245.04.01.02.05580.0690none421315120.0
1233.000214.0420.01Mac n Cheese, Lasagna, Pizzahappiness, they are some of my favorite foods3.071.012.0I don't eat as much on a daily basis since coming to college.1852.021.03.0Store manager at Giant EagleItalian13.01511164.0151.01.02.0Receptionist for a medical supply company41.0231.02.02.02.01940.0500Unknow311315135.0
1243.900110.0315.02Chocolates, pizza, and Ritz.hormones, Premenstrual syndrome.NaN53.011.0I have learned to eat more vegetables.2512.032.04.0JournalistHISPANIC CUISINE.21.01332335.0235.02.03.0House-wife51.0332.03.01.02.02725.0345Unknow42575135.0